Linda Krestanova3 min

The Skills of Our Data Science Team

EngineeringFeb 3, 2022

It can be a little tricky to understand the skillset of a Data Science team. The specifications are not only complex but also dependent on the business in question, its primary focus and the technology it utilizes.

At STRV, Data Science (DS) is a multidisciplinary field because we work with clients spanning countless industries. On our projects, DS is viewed as a process of understanding and compiling data to solve a specific business problem for the client.

For that, we have a team of data professionals consisting mainly of Machine Learning Engineers and Data Engineers with uniquely defined responsibilities and skills.

Together, these engineers excel at understanding clients’ problems and applying relevant technology to deliver any DS solution — the most common being recommendation engines, content moderation, chatbots, business intelligence, solutions to computer vision or natural language processing problems, data analysis and business process automation.

STRV Data Science Department Lead, Jan Maly, explains the abilities of our DS team, how they’re applied on specific projects and what the team’s capabilities mean for our clients.

The Machine Learning Engineer

STRV Machine Learning (ML) Engineers are sort of the know-it-alls skilled at building end-to-end AI/ML solutions. They can understand and communicate the business problem at hand, prepare a relevant dataset, create a solution based on the data and deploy it. They make sure that the solution is reliable and monitored.

In practice, this expertise is best applied to solving various computer vision and natural language processing problems such as content moderation or chatbots, building recommendation and search engines or performing advanced data analysis.

This role is often called Data Scientist. However, we prefer ML Engineer because it emphasizes solving the project end to end and because a Data Scientist isn’t usually responsible for deploying solutions into production.

The Data Engineer

STRV Data Engineers excel at solving data problems and building data solutions. They can understand and communicate the business problem at hand, connect it to a variety of data sources, comprehend the data and turn it into something valuable for the business.

In practice, this expertise is best applied to building data pipelines, data storage and data modeling. Data Engineers are also responsible for business intelligence, reporting and analytics. By using a modern data stack, we can build data pipelines more efficiently and simultaneously focus on the analytical/business part of the problem.

Our Data Engineers’ role combines the responsibilities of two positions found within our industry: Data Analyst and Data Engineer.

What You Can Expect From Our DS Team

The requirements we have for our DS team go beyond technological proficiency. We look for engineers that have a developed sense of big picture thinking.

Our engineers are able to dissect and understand the business context behind a client’s needs because many of our clients don’t necessarily know what they need from us. Instead, they have an idea of the end result and require our guidance to get there.

For this reason, we don’t just ask ourselves, “What’s the best solution out there?” We dive into a client’s ambitions, analyze all options and figure out the right solution, tailored to the issue at hand.

How do we find the “right” solution?

In short, our DS team’s unique strength lies in taking their primary expertise — a mathematical and statistical background, computer science skillset and the ability to work with complex data — and applying it to the given business problem.

We do so by dividing the project into several stages with usable deliverables. Our goal is always to deliver some business value as soon as possible.

The Data Science Team Vision

Jan Maly believes that the Data Science team’s role is to be a fundamental force behind STRV’s evolution.

“By building and expanding a stellar team, we play an increasingly important role in defining STRV's future. We want to continue helping people understand that data science is now vital for gaining a competitive edge and scaling business and, when we apply the relevant technology the right way, it’s also absolutely attainable and affordable,” Jan explains.

“That’s how we make our clients’ biggest dreams become a reality, which is the meaningful impact that drives everyone on the team.”

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